Overview

Dataset statistics

Number of variables50
Number of observations11033
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory332.0 B

Variable types

Numeric21
Categorical29

Alerts

subCategoryB has constant value ""Constant
isnewincident has constant value ""Constant
Unnamed 0 is highly overall correlated with id and 1 other fieldsHigh correlation
id is highly overall correlated with Unnamed 0 and 2 other fieldsHigh correlation
longitude is highly overall correlated with distance_to_CBDHigh correlation
subCategoryA is highly overall correlated with multi_vehicleHigh correlation
displayname is highly overall correlated with maincategory and 2 other fieldsHigh correlation
adviceA is highly overall correlated with closuretype and 1 other fieldsHigh correlation
mainstreet is highly overall correlated with Motorway CrewHigh correlation
lanes is highly overall correlated with closuretypeHigh correlation
count_attending is highly overall correlated with Tow Truck and 2 other fieldsHigh correlation
distance_to_CBD is highly overall correlated with longitudeHigh correlation
starttime_month is highly overall correlated with Unnamed 0 and 3 other fieldsHigh correlation
maincategory is highly overall correlated with displayname and 3 other fieldsHigh correlation
closuretype is highly overall correlated with adviceA and 3 other fieldsHigh correlation
lanesaffected is highly overall correlated with closuretypeHigh correlation
multi_vehicle is highly overall correlated with subCategoryA and 2 other fieldsHigh correlation
Tow Truck is highly overall correlated with count_attendingHigh correlation
Motorway Crew is highly overall correlated with mainstreetHigh correlation
Emergency services is highly overall correlated with displayname and 2 other fieldsHigh correlation
Other is highly overall correlated with adviceA and 1 other fieldsHigh correlation
starttime_year is highly overall correlated with id and 2 other fieldsHigh correlation
isMajor is highly imbalanced (71.7%)Imbalance
Crime Scene is highly imbalanced (97.4%)Imbalance
Bus company is highly imbalanced (99.6%)Imbalance
Tyre fitter is highly imbalanced (99.1%)Imbalance
Mechanic is highly imbalanced (81.0%)Imbalance
Sydney Ports is highly imbalanced (99.7%)Imbalance
Local Council is highly imbalanced (99.7%)Imbalance
NRMA is highly imbalanced (98.9%)Imbalance
Heavy Vehicle Inspectors is highly imbalanced (99.0%)Imbalance
Sweeper is highly imbalanced (99.5%)Imbalance
Heavy vehicle tow truck is highly imbalanced (71.0%)Imbalance
Tyre Fitter is highly imbalanced (99.7%)Imbalance
Utility Company is highly imbalanced (93.7%)Imbalance
Roadside Assistance is highly imbalanced (99.9%)Imbalance
Helicopter is highly imbalanced (98.9%)Imbalance
Crash Investigation Unit is highly imbalanced (91.8%)Imbalance
starttime_year is highly imbalanced (93.0%)Imbalance
Unnamed 0 has unique valuesUnique
adviceA has 213 (1.9%) zerosZeros
adviceB has 2311 (20.9%) zerosZeros
otherAdvice has 10273 (93.1%) zerosZeros
direction has 140 (1.3%) zerosZeros
starttime_weekday has 1605 (14.5%) zerosZeros
starttime_hour has 116 (1.1%) zerosZeros
starttime_minute has 221 (2.0%) zerosZeros

Reproduction

Analysis started2024-03-11 23:06:22.163511
Analysis finished2024-03-11 23:07:59.823424
Duration1 minute and 37.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11033
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8281.9906
Minimum1
Maximum17102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.3 KiB
2024-03-12T10:07:59.953518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile569.6
Q14111
median8109
Q312599
95-th percentile15847.2
Maximum17102
Range17101
Interquartile range (IQR)8488

Descriptive statistics

Standard deviation4914.0312
Coefficient of variation (CV)0.59333939
Kurtosis-1.2007914
Mean8281.9906
Median Absolute Deviation (MAD)4327
Skewness0.013386271
Sum91375202
Variance24147703
MonotonicityStrictly increasing
2024-03-12T10:08:00.192658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
10985 1
 
< 0.1%
10965 1
 
< 0.1%
10969 1
 
< 0.1%
10972 1
 
< 0.1%
10974 1
 
< 0.1%
10978 1
 
< 0.1%
10979 1
 
< 0.1%
10982 1
 
< 0.1%
10984 1
 
< 0.1%
Other values (11023) 11023
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
17102 1
< 0.1%
17101 1
< 0.1%
17100 1
< 0.1%
17099 1
< 0.1%
17098 1
< 0.1%
17096 1
< 0.1%
17095 1
< 0.1%
17091 1
< 0.1%
17089 1
< 0.1%
17086 1
< 0.1%

id
Real number (ℝ)

HIGH CORRELATION 

Distinct5706
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118899.1
Minimum43155
Maximum128338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.3 KiB
2024-03-12T10:08:00.433518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum43155
5-th percentile110328
Q1113830
median119307
Q3123824
95-th percentile127550
Maximum128338
Range85183
Interquartile range (IQR)9994

Descriptive statistics

Standard deviation6187.9831
Coefficient of variation (CV)0.052043988
Kurtosis21.167108
Mean118899.1
Median Absolute Deviation (MAD)4917
Skewness-2.0038434
Sum1.3118137 × 109
Variance38291135
MonotonicityNot monotonic
2024-03-12T10:08:00.670800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108252 66
 
0.6%
101976 33
 
0.3%
120833 26
 
0.2%
95659 22
 
0.2%
122769 21
 
0.2%
122388 20
 
0.2%
119464 19
 
0.2%
125858 18
 
0.2%
128172 17
 
0.2%
125825 17
 
0.2%
Other values (5696) 10774
97.7%
ValueCountFrequency (%)
43155 11
 
0.1%
95659 22
 
0.2%
100553 11
 
0.1%
101976 33
0.3%
107684 11
 
0.1%
108252 66
0.6%
109547 11
 
0.1%
109551 1
 
< 0.1%
109553 1
 
< 0.1%
109556 4
 
< 0.1%
ValueCountFrequency (%)
128338 1
 
< 0.1%
128337 1
 
< 0.1%
128335 1
 
< 0.1%
128330 1
 
< 0.1%
128327 2
 
< 0.1%
128326 2
 
< 0.1%
128324 8
0.1%
128322 2
 
< 0.1%
128319 1
 
< 0.1%
128316 1
 
< 0.1%

maincategory
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
1
5539 
0
5385 
2
 
109

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 5539
50.2%
0 5385
48.8%
2 109
 
1.0%

Length

2024-03-12T10:08:00.868704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:01.072133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5539
50.2%
0 5385
48.8%
2 109
 
1.0%

Most occurring characters

ValueCountFrequency (%)
1 5539
50.2%
0 5385
48.8%
2 109
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5539
50.2%
0 5385
48.8%
2 109
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5539
50.2%
0 5385
48.8%
2 109
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5539
50.2%
0 5385
48.8%
2 109
 
1.0%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct4989
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.10729
Minimum150.87534
Maximum151.30365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.3 KiB
2024-03-12T10:08:01.274893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum150.87534
5-th percentile150.95975
Q1151.03752
median151.1133
Q3151.18945
95-th percentile151.22549
Maximum151.30365
Range0.42831444
Interquartile range (IQR)0.15192735

Descriptive statistics

Standard deviation0.089973298
Coefficient of variation (CV)0.0005954266
Kurtosis-1.0091117
Mean151.10729
Median Absolute Deviation (MAD)0.075967258
Skewness-0.1883372
Sum1667166.7
Variance0.0080951943
MonotonicityNot monotonic
2024-03-12T10:08:01.512588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151.2663884 66
 
0.6%
151.0210045 33
 
0.3%
151.1178416 26
 
0.2%
150.9576753 25
 
0.2%
151.2109853 23
 
0.2%
151.0738178 22
 
0.2%
151.0834442 21
 
0.2%
151.1734685 21
 
0.2%
151.1714301 20
 
0.2%
150.9908029 20
 
0.2%
Other values (4979) 10756
97.5%
ValueCountFrequency (%)
150.8753388 1
 
< 0.1%
150.9155738 1
 
< 0.1%
150.9170244 1
 
< 0.1%
150.9174042 8
0.1%
150.9175208 3
 
< 0.1%
150.9175281 2
 
< 0.1%
150.9175286 1
 
< 0.1%
150.9175313 1
 
< 0.1%
150.9176935 1
 
< 0.1%
150.9177088 2
 
< 0.1%
ValueCountFrequency (%)
151.3036532 2
 
< 0.1%
151.2991724 2
 
< 0.1%
151.2983989 5
< 0.1%
151.2980048 5
< 0.1%
151.2976545 1
 
< 0.1%
151.2972783 1
 
< 0.1%
151.2971827 1
 
< 0.1%
151.2966473 1
 
< 0.1%
151.2961147 1
 
< 0.1%
151.2953047 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct5010
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-33.868494
Minimum-34.085829
Maximum-33.687838
Zeros0
Zeros (%)0.0%
Negative11033
Negative (%)100.0%
Memory size86.3 KiB
2024-03-12T10:08:01.760552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-34.085829
5-th percentile-33.969869
Q1-33.929238
median-33.871086
Q3-33.81791
95-th percentile-33.741737
Maximum-33.687838
Range0.3979912
Interquartile range (IQR)0.11132752

Descriptive statistics

Standard deviation0.072159971
Coefficient of variation (CV)-0.0021305928
Kurtosis-0.25386415
Mean-33.868494
Median Absolute Deviation (MAD)0.054784235
Skewness0.036640587
Sum-373671.09
Variance0.0052070614
MonotonicityNot monotonic
2024-03-12T10:08:01.994570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-33.8714826 66
 
0.6%
-33.83135102 33
 
0.3%
-33.70461825 26
 
0.2%
-33.8944404 25
 
0.2%
-33.83633 23
 
0.2%
-33.85546713 22
 
0.2%
-33.9525606 21
 
0.2%
-33.9459451 21
 
0.2%
-33.7219503 20
 
0.2%
-33.9298764 20
 
0.2%
Other values (5000) 10756
97.5%
ValueCountFrequency (%)
-34.0858287 1
 
< 0.1%
-34.08142688 1
 
< 0.1%
-34.07990445 5
< 0.1%
-34.07950996 1
 
< 0.1%
-34.0794868 2
 
< 0.1%
-34.07555601 1
 
< 0.1%
-34.0746229 4
< 0.1%
-34.07458291 1
 
< 0.1%
-34.07295217 1
 
< 0.1%
-34.07048369 3
< 0.1%
ValueCountFrequency (%)
-33.6878375 5
< 0.1%
-33.69051864 4
< 0.1%
-33.69142094 1
 
< 0.1%
-33.69230216 1
 
< 0.1%
-33.69240333 4
< 0.1%
-33.69273277 2
 
< 0.1%
-33.69277174 3
< 0.1%
-33.6935721 1
 
< 0.1%
-33.69551576 3
< 0.1%
-33.6966894 1
 
< 0.1%

duration
Real number (ℝ)

Distinct10831
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.723214
Minimum1.0014333
Maximum1038.0941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.3 KiB
2024-03-12T10:08:02.261551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0014333
5-th percentile3.3437633
Q113.701283
median28.037333
Q352.261333
95-th percentile141.58101
Maximum1038.0941
Range1037.0927
Interquartile range (IQR)38.56005

Descriptive statistics

Standard deviation56.647702
Coefficient of variation (CV)1.2666286
Kurtosis28.96921
Mean44.723214
Median Absolute Deviation (MAD)16.889233
Skewness4.0700719
Sum493431.22
Variance3208.9621
MonotonicityNot monotonic
2024-03-12T10:08:02.506597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.25456667 11
 
0.1%
48.30671667 11
 
0.1%
150.20255 11
 
0.1%
20.11616667 11
 
0.1%
66.13966667 11
 
0.1%
12.9415 11
 
0.1%
11.64028333 11
 
0.1%
33.9305 11
 
0.1%
126.4965 11
 
0.1%
220.7816667 11
 
0.1%
Other values (10821) 10923
99.0%
ValueCountFrequency (%)
1.001433333 1
< 0.1%
1.005833333 1
< 0.1%
1.006783333 1
< 0.1%
1.00705 1
< 0.1%
1.018333333 1
< 0.1%
1.019216667 1
< 0.1%
1.02345 1
< 0.1%
1.023616667 1
< 0.1%
1.029 1
< 0.1%
1.029383333 1
< 0.1%
ValueCountFrequency (%)
1038.094117 1
< 0.1%
884.46 1
< 0.1%
597.9344 1
< 0.1%
594.4488833 1
< 0.1%
588.5066 1
< 0.1%
577.1800667 1
< 0.1%
557.3058833 1
< 0.1%
536.8422167 1
< 0.1%
518.7523833 1
< 0.1%
509.5341167 1
< 0.1%

subCategoryA
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.667815
Minimum0
Maximum45
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:02.739579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q333
95-th percentile41
Maximum45
Range45
Interquartile range (IQR)26

Descriptive statistics

Standard deviation13.798349
Coefficient of variation (CV)0.73915183
Kurtosis-1.2471476
Mean18.667815
Median Absolute Deviation (MAD)13
Skewness0.28810943
Sum205962
Variance190.39445
MonotonicityNot monotonic
2024-03-12T10:08:02.960329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
14 3186
28.9%
1 2130
19.3%
33 1367
12.4%
41 1258
 
11.4%
27 549
 
5.0%
4 426
 
3.9%
36 376
 
3.4%
10 301
 
2.7%
17 212
 
1.9%
29 194
 
1.8%
Other values (36) 1034
 
9.4%
ValueCountFrequency (%)
0 11
 
0.1%
1 2130
19.3%
2 40
 
0.4%
3 23
 
0.2%
4 426
 
3.9%
5 112
 
1.0%
6 3
 
< 0.1%
7 87
 
0.8%
8 6
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
43 3
 
< 0.1%
42 111
 
1.0%
41 1258
11.4%
40 4
 
< 0.1%
39 36
 
0.3%
38 36
 
0.3%
37 7
 
0.1%
36 376
 
3.4%

subCategoryB
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11033 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11033
100.0%

Length

2024-03-12T10:08:03.157761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:03.336011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11033
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11033
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11033
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11033
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11033
100.0%

displayname
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.958579
Minimum0
Maximum156
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:03.495198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median48
Q362
95-th percentile120
Maximum156
Range156
Interquartile range (IQR)57

Descriptive statistics

Standard deviation37.729133
Coefficient of variation (CV)0.82093777
Kurtosis-0.43830697
Mean45.958579
Median Absolute Deviation (MAD)28
Skewness0.6635413
Sum507061
Variance1423.4875
MonotonicityNot monotonic
2024-03-12T10:08:03.730506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3077
27.9%
51 2012
18.2%
33 1478
13.4%
76 687
 
6.2%
110 541
 
4.9%
60 402
 
3.6%
130 363
 
3.3%
3 286
 
2.6%
94 207
 
1.9%
30 192
 
1.7%
Other values (147) 1788
16.2%
ValueCountFrequency (%)
0 8
 
0.1%
1 2
 
< 0.1%
2 74
 
0.7%
3 286
 
2.6%
4 2
 
< 0.1%
5 3077
27.9%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
156 1
 
< 0.1%
155 1
 
< 0.1%
154 2
< 0.1%
153 3
< 0.1%
152 1
 
< 0.1%
151 2
< 0.1%
150 2
< 0.1%
149 3
< 0.1%
148 1
 
< 0.1%
147 1
 
< 0.1%

isMajor
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
10490 
1
 
543

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10490
95.1%
1 543
 
4.9%

Length

2024-03-12T10:08:03.959243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:04.144780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10490
95.1%
1 543
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 10490
95.1%
1 543
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10490
95.1%
1 543
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10490
95.1%
1 543
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10490
95.1%
1 543
 
4.9%

adviceA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9921146
Minimum0
Maximum18
Zeros213
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:04.290862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median6
Q313
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.5548816
Coefficient of variation (CV)0.56992196
Kurtosis-1.2559598
Mean7.9921146
Median Absolute Deviation (MAD)5
Skewness-0.037877055
Sum88177
Variance20.746947
MonotonicityNot monotonic
2024-03-12T10:08:04.468012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6 4142
37.5%
13 3559
32.3%
1 1414
 
12.8%
7 711
 
6.4%
14 371
 
3.4%
2 273
 
2.5%
0 213
 
1.9%
15 185
 
1.7%
16 100
 
0.9%
12 18
 
0.2%
Other values (9) 47
 
0.4%
ValueCountFrequency (%)
0 213
 
1.9%
1 1414
 
12.8%
2 273
 
2.5%
3 3
 
< 0.1%
4 15
 
0.1%
5 10
 
0.1%
6 4142
37.5%
7 711
 
6.4%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 2
 
< 0.1%
16 100
 
0.9%
15 185
 
1.7%
14 371
 
3.4%
13 3559
32.3%
12 18
 
0.2%
11 11
 
0.1%
10 1
 
< 0.1%
9 2
 
< 0.1%

adviceB
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.656757
Minimum0
Maximum44
Zeros2311
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:04.692612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12
Q312
95-th percentile29
Maximum44
Range44
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.521849
Coefficient of variation (CV)0.9026395
Kurtosis-0.15699011
Mean11.656757
Median Absolute Deviation (MAD)11
Skewness0.75652993
Sum128609
Variance110.70931
MonotonicityNot monotonic
2024-03-12T10:08:04.909196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
12 4733
42.9%
0 2311
20.9%
29 1393
 
12.6%
1 1360
 
12.3%
20 410
 
3.7%
37 176
 
1.6%
13 97
 
0.9%
38 75
 
0.7%
17 48
 
0.4%
28 41
 
0.4%
Other values (35) 389
 
3.5%
ValueCountFrequency (%)
0 2311
20.9%
1 1360
12.3%
2 2
 
< 0.1%
3 32
 
0.3%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 32
 
0.3%
7 4
 
< 0.1%
8 15
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
44 13
 
0.1%
43 10
 
0.1%
42 14
 
0.1%
41 14
 
0.1%
40 24
 
0.2%
39 5
 
< 0.1%
38 75
0.7%
37 176
1.6%
36 11
 
0.1%
35 3
 
< 0.1%

otherAdvice
Real number (ℝ)

ZEROS 

Distinct350
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.765612
Minimum0
Maximum349
Zeros10273
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:05.128264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile94
Maximum349
Range349
Interquartile range (IQR)0

Descriptive statistics

Standard deviation50.89013
Coefficient of variation (CV)4.3253278
Kurtosis22.733304
Mean11.765612
Median Absolute Deviation (MAD)0
Skewness4.7665361
Sum129810
Variance2589.8053
MonotonicityNot monotonic
2024-03-12T10:08:05.363988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10273
93.1%
94 38
 
0.3%
25 11
 
0.1%
96 10
 
0.1%
92 9
 
0.1%
93 9
 
0.1%
204 9
 
0.1%
82 8
 
0.1%
259 8
 
0.1%
179 7
 
0.1%
Other values (340) 651
 
5.9%
ValueCountFrequency (%)
0 10273
93.1%
1 2
 
< 0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
349 5
< 0.1%
348 1
 
< 0.1%
347 5
< 0.1%
346 5
< 0.1%
345 2
 
< 0.1%
344 3
< 0.1%
343 4
< 0.1%
342 2
 
< 0.1%
341 2
 
< 0.1%
340 3
< 0.1%

closuretype
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
2
6858 
0
3478 
1
697 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row0

Common Values

ValueCountFrequency (%)
2 6858
62.2%
0 3478
31.5%
1 697
 
6.3%

Length

2024-03-12T10:08:05.586115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:05.760983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 6858
62.2%
0 3478
31.5%
1 697
 
6.3%

Most occurring characters

ValueCountFrequency (%)
2 6858
62.2%
0 3478
31.5%
1 697
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6858
62.2%
0 3478
31.5%
1 697
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 6858
62.2%
0 3478
31.5%
1 697
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6858
62.2%
0 3478
31.5%
1 697
 
6.3%

direction
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3804042
Minimum0
Maximum8
Zeros140
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:05.924981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2814645
Coefficient of variation (CV)0.42403216
Kurtosis-0.92975466
Mean5.3804042
Median Absolute Deviation (MAD)2
Skewness-0.4870503
Sum59362
Variance5.2050801
MonotonicityNot monotonic
2024-03-12T10:08:06.096915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 2637
23.9%
5 2558
23.2%
8 2525
22.9%
3 2296
20.8%
1 743
 
6.7%
0 140
 
1.3%
2 130
 
1.2%
6 3
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 140
 
1.3%
1 743
 
6.7%
2 130
 
1.2%
3 2296
20.8%
4 1
 
< 0.1%
5 2558
23.2%
6 3
 
< 0.1%
7 2637
23.9%
8 2525
22.9%
ValueCountFrequency (%)
8 2525
22.9%
7 2637
23.9%
6 3
 
< 0.1%
5 2558
23.2%
4 1
 
< 0.1%
3 2296
20.8%
2 130
 
1.2%
1 743
 
6.7%
0 140
 
1.3%

mainstreet
Real number (ℝ)

HIGH CORRELATION 

Distinct577
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314.19741
Minimum0
Maximum576
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:06.321415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46
Q1180
median319
Q3441
95-th percentile550
Maximum576
Range576
Interquartile range (IQR)261

Descriptive statistics

Standard deviation159.72872
Coefficient of variation (CV)0.50837058
Kurtosis-1.1465131
Mean314.19741
Median Absolute Deviation (MAD)125
Skewness-0.14342096
Sum3466540
Variance25513.264
MonotonicityNot monotonic
2024-03-12T10:08:06.536945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
293 501
 
4.5%
336 427
 
3.9%
427 390
 
3.5%
527 353
 
3.2%
441 323
 
2.9%
220 306
 
2.8%
180 254
 
2.3%
136 251
 
2.3%
319 243
 
2.2%
239 242
 
2.2%
Other values (567) 7743
70.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 13
0.1%
2 6
 
0.1%
3 1
 
< 0.1%
4 19
0.2%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 16
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
576 5
 
< 0.1%
575 2
 
< 0.1%
574 113
1.0%
573 1
 
< 0.1%
572 58
0.5%
571 1
 
< 0.1%
570 11
 
0.1%
569 1
 
< 0.1%
568 5
 
< 0.1%
567 18
 
0.2%

lanesaffected
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
1.0
6223 
-1.0
3848 
2.0
885 
3.0
 
50
4.0
 
27

Length

Max length4
Median length3
Mean length3.3487719
Min length3

Characters and Unicode

Total characters36947
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
1.0 6223
56.4%
-1.0 3848
34.9%
2.0 885
 
8.0%
3.0 50
 
0.5%
4.0 27
 
0.2%

Length

2024-03-12T10:08:06.779912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:07.002768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10071
91.3%
2.0 885
 
8.0%
3.0 50
 
0.5%
4.0 27
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 11033
29.9%
0 11033
29.9%
1 10071
27.3%
- 3848
 
10.4%
2 885
 
2.4%
3 50
 
0.1%
4 27
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22066
59.7%
Other Punctuation 11033
29.9%
Dash Punctuation 3848
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11033
50.0%
1 10071
45.6%
2 885
 
4.0%
3 50
 
0.2%
4 27
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 11033
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 11033
29.9%
0 11033
29.9%
1 10071
27.3%
- 3848
 
10.4%
2 885
 
2.4%
3 50
 
0.1%
4 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 11033
29.9%
0 11033
29.9%
1 10071
27.3%
- 3848
 
10.4%
2 885
 
2.4%
3 50
 
0.1%
4 27
 
0.1%

lanes
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4800145
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative4287
Negative (%)38.9%
Memory size86.3 KiB
2024-03-12T10:08:07.186885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2
Q33
95-th percentile4
Maximum10
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.095392
Coefficient of variation (CV)1.4157915
Kurtosis-1.4413827
Mean1.4800145
Median Absolute Deviation (MAD)2
Skewness-0.09271848
Sum16329
Variance4.3906674
MonotonicityNot monotonic
2024-03-12T10:08:07.355231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-1 4287
38.9%
3 3293
29.8%
2 1781
16.1%
4 1233
 
11.2%
5 348
 
3.2%
6 64
 
0.6%
1 13
 
0.1%
7 8
 
0.1%
8 5
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
-1 4287
38.9%
1 13
 
0.1%
2 1781
16.1%
3 3293
29.8%
4 1233
 
11.2%
5 348
 
3.2%
6 64
 
0.6%
7 8
 
0.1%
8 5
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 5
 
< 0.1%
7 8
 
0.1%
6 64
 
0.6%
5 348
 
3.2%
4 1233
 
11.2%
3 3293
29.8%
2 1781
16.1%
1 13
 
0.1%
-1 4287
38.9%

suburb
Real number (ℝ)

Distinct328
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.16115
Minimum0
Maximum327
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:07.578705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q1103
median182
Q3255
95-th percentile309
Maximum327
Range327
Interquartile range (IQR)152

Descriptive statistics

Standard deviation93.255669
Coefficient of variation (CV)0.53854844
Kurtosis-1.0403279
Mean173.16115
Median Absolute Deviation (MAD)75
Skewness-0.27592014
Sum1910487
Variance8696.6198
MonotonicityNot monotonic
2024-03-12T10:08:07.820165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181 379
 
3.4%
288 352
 
3.2%
216 238
 
2.2%
5 202
 
1.8%
191 191
 
1.7%
186 163
 
1.5%
196 162
 
1.5%
29 156
 
1.4%
262 153
 
1.4%
124 153
 
1.4%
Other values (318) 8884
80.5%
ValueCountFrequency (%)
0 26
 
0.2%
1 8
 
0.1%
2 5
 
< 0.1%
3 15
 
0.1%
4 1
 
< 0.1%
5 202
1.8%
6 68
 
0.6%
7 110
1.0%
8 12
 
0.1%
9 113
1.0%
ValueCountFrequency (%)
327 1
 
< 0.1%
326 1
 
< 0.1%
325 31
 
0.3%
324 61
0.6%
323 11
 
0.1%
322 11
 
0.1%
321 81
0.7%
320 7
 
0.1%
319 3
 
< 0.1%
318 21
 
0.2%

ended
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
1
5683 
0
5350 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5683
51.5%
0 5350
48.5%

Length

2024-03-12T10:08:08.043479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:08.237994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5683
51.5%
0 5350
48.5%

Most occurring characters

ValueCountFrequency (%)
1 5683
51.5%
0 5350
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5683
51.5%
0 5350
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5683
51.5%
0 5350
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5683
51.5%
0 5350
48.5%

isnewincident
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11033 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11033
100.0%

Length

2024-03-12T10:08:08.397086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:08.569844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11033
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11033
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11033
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11033
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11033
100.0%

count_attending
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.542101
Minimum0
Maximum5
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size86.3 KiB
2024-03-12T10:08:08.695344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.70663835
Coefficient of variation (CV)0.45823092
Kurtosis1.0164755
Mean1.542101
Median Absolute Deviation (MAD)0
Skewness1.1589406
Sum17014
Variance0.49933775
MonotonicityNot monotonic
2024-03-12T10:08:08.870562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 6247
56.6%
2 3705
33.6%
3 933
 
8.5%
4 127
 
1.2%
0 11
 
0.1%
5 10
 
0.1%
ValueCountFrequency (%)
0 11
 
0.1%
1 6247
56.6%
2 3705
33.6%
3 933
 
8.5%
4 127
 
1.2%
5 10
 
0.1%
ValueCountFrequency (%)
5 10
 
0.1%
4 127
 
1.2%
3 933
 
8.5%
2 3705
33.6%
1 6247
56.6%
0 11
 
0.1%

distance_to_CBD
Real number (ℝ)

HIGH CORRELATION 

Distinct5023
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.860733
Minimum0.10471289
Maximum32.214274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.3 KiB
2024-03-12T10:08:09.068463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.10471289
5-th percentile1.6071321
Q16.6379596
median12.980484
Q319.090044
95-th percentile24.377243
Maximum32.214274
Range32.109562
Interquartile range (IQR)12.452085

Descriptive statistics

Standard deviation7.2631172
Coefficient of variation (CV)0.56475143
Kurtosis-1.0461336
Mean12.860733
Median Absolute Deviation (MAD)6.1947907
Skewness0.058564139
Sum141892.46
Variance52.752871
MonotonicityNot monotonic
2024-03-12T10:08:09.309677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.530487649 66
 
0.6%
17.79789884 33
 
0.3%
20.43236091 26
 
0.2%
23.15253418 25
 
0.2%
4.114164888 23
 
0.2%
12.44843767 22
 
0.2%
14.39534087 21
 
0.2%
8.628185843 21
 
0.2%
20.92900818 20
 
0.2%
17.09540509 20
 
0.2%
Other values (5013) 10756
97.5%
ValueCountFrequency (%)
0.1047128944 1
 
< 0.1%
0.1368488833 6
0.1%
0.1686331612 1
 
< 0.1%
0.2263128854 1
 
< 0.1%
0.2760782153 1
 
< 0.1%
0.2899489754 1
 
< 0.1%
0.3010319453 2
 
< 0.1%
0.303265914 3
< 0.1%
0.3140012585 1
 
< 0.1%
0.3175158395 1
 
< 0.1%
ValueCountFrequency (%)
32.21427446 1
 
< 0.1%
29.20514309 1
 
< 0.1%
29.20261163 5
< 0.1%
29.17982264 2
 
< 0.1%
29.14541148 3
< 0.1%
29.07165961 1
 
< 0.1%
29.06837068 6
0.1%
29.04702692 1
 
< 0.1%
28.98804487 4
< 0.1%
28.98747383 1
 
< 0.1%

multi_vehicle
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
6233 
1
4800 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6233
56.5%
1 4800
43.5%

Length

2024-03-12T10:08:09.517821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:09.690614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6233
56.5%
1 4800
43.5%

Most occurring characters

ValueCountFrequency (%)
0 6233
56.5%
1 4800
43.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6233
56.5%
1 4800
43.5%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6233
56.5%
1 4800
43.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6233
56.5%
1 4800
43.5%

Crime Scene
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11004 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11004
99.7%
1 29
 
0.3%

Length

2024-03-12T10:08:09.851986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:10.033875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11004
99.7%
1 29
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 11004
99.7%
1 29
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11004
99.7%
1 29
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11004
99.7%
1 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11004
99.7%
1 29
 
0.3%

Tow Truck
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
9677 
1
1356 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9677
87.7%
1 1356
 
12.3%

Length

2024-03-12T10:08:10.185814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:10.365985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9677
87.7%
1 1356
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 9677
87.7%
1 1356
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9677
87.7%
1 1356
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9677
87.7%
1 1356
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9677
87.7%
1 1356
 
12.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
1
6310 
0
4723 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 6310
57.2%
0 4723
42.8%

Length

2024-03-12T10:08:10.522358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:10.703981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 6310
57.2%
0 4723
42.8%

Most occurring characters

ValueCountFrequency (%)
1 6310
57.2%
0 4723
42.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6310
57.2%
0 4723
42.8%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6310
57.2%
0 4723
42.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6310
57.2%
0 4723
42.8%

Bus company
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11030 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11030
> 99.9%
1 3
 
< 0.1%

Length

2024-03-12T10:08:10.861744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:11.636350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11030
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11030
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11030
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11030
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11030
> 99.9%
1 3
 
< 0.1%

Tyre fitter
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11025 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11025
99.9%
1 8
 
0.1%

Length

2024-03-12T10:08:11.793946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:11.982341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11025
99.9%
1 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11025
99.9%
1 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11025
99.9%
1 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11025
99.9%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11025
99.9%
1 8
 
0.1%

Mechanic
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
10712 
1
 
321

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10712
97.1%
1 321
 
2.9%

Length

2024-03-12T10:08:12.123450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:12.303547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10712
97.1%
1 321
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 10712
97.1%
1 321
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10712
97.1%
1 321
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10712
97.1%
1 321
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10712
97.1%
1 321
 
2.9%

Sydney Ports
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11031 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Length

2024-03-12T10:08:12.460248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:12.632520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Local Council
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11031 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Length

2024-03-12T10:08:12.796493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:12.970994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

NRMA
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11022 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Length

2024-03-12T10:08:13.121638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:13.310091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Heavy Vehicle Inspectors
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11023 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11023
99.9%
1 10
 
0.1%

Length

2024-03-12T10:08:13.456213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:13.629007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11023
99.9%
1 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11023
99.9%
1 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11023
99.9%
1 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11023
99.9%
1 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11023
99.9%
1 10
 
0.1%

Sweeper
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11029 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11029
> 99.9%
1 4
 
< 0.1%

Length

2024-03-12T10:08:13.786138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:13.973779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11029
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11029
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11029
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11029
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11029
> 99.9%
1 4
 
< 0.1%

Heavy vehicle tow truck
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
10472 
1
 
561

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10472
94.9%
1 561
 
5.1%

Length

2024-03-12T10:08:14.125423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:14.298242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10472
94.9%
1 561
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 10472
94.9%
1 561
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10472
94.9%
1 561
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10472
94.9%
1 561
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10472
94.9%
1 561
 
5.1%

Tyre Fitter
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11031 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Length

2024-03-12T10:08:14.451902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:14.640315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11031
> 99.9%
1 2
 
< 0.1%

Utility Company
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
10951 
1
 
82

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10951
99.3%
1 82
 
0.7%

Length

2024-03-12T10:08:14.797475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:14.977408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10951
99.3%
1 82
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 10951
99.3%
1 82
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10951
99.3%
1 82
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10951
99.3%
1 82
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10951
99.3%
1 82
 
0.7%

Motorway Crew
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
9569 
1
1464 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9569
86.7%
1 1464
 
13.3%

Length

2024-03-12T10:08:15.118521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:15.306942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9569
86.7%
1 1464
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 9569
86.7%
1 1464
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9569
86.7%
1 1464
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9569
86.7%
1 1464
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9569
86.7%
1 1464
 
13.3%

Emergency services
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
5907 
1
5126 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5907
53.5%
1 5126
46.5%

Length

2024-03-12T10:08:15.455834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:15.644286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5907
53.5%
1 5126
46.5%

Most occurring characters

ValueCountFrequency (%)
0 5907
53.5%
1 5126
46.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5907
53.5%
1 5126
46.5%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5907
53.5%
1 5126
46.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5907
53.5%
1 5126
46.5%

Roadside Assistance
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11032 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11032
> 99.9%
1 1
 
< 0.1%

Length

2024-03-12T10:08:15.801478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:15.982487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11032
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11032
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11032
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11032
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11032
> 99.9%
1 1
 
< 0.1%

Other
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
9435 
1
1598 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 9435
85.5%
1 1598
 
14.5%

Length

2024-03-12T10:08:16.130524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:16.303311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9435
85.5%
1 1598
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 9435
85.5%
1 1598
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9435
85.5%
1 1598
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9435
85.5%
1 1598
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9435
85.5%
1 1598
 
14.5%

Helicopter
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
11022 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Length

2024-03-12T10:08:16.459983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:16.639023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11022
99.9%
1 11
 
0.1%

Crash Investigation Unit
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
0
10920 
1
 
113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10920
99.0%
1 113
 
1.0%

Length

2024-03-12T10:08:16.780012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:16.969411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10920
99.0%
1 113
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 10920
99.0%
1 113
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11033
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10920
99.0%
1 113
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11033
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10920
99.0%
1 113
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10920
99.0%
1 113
 
1.0%

starttime_year
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.3 KiB
2022
10879 
2021
 
143
2019
 
11

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters44132
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10879
98.6%
2021 143
 
1.3%
2019 11
 
0.1%

Length

2024-03-12T10:08:17.118634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-12T10:08:17.307065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2022 10879
98.6%
2021 143
 
1.3%
2019 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 32934
74.6%
0 11033
 
25.0%
1 154
 
0.3%
9 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44132
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 32934
74.6%
0 11033
 
25.0%
1 154
 
0.3%
9 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 44132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 32934
74.6%
0 11033
 
25.0%
1 154
 
0.3%
9 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 32934
74.6%
0 11033
 
25.0%
1 154
 
0.3%
9 11
 
< 0.1%

starttime_month
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0861053
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:17.448131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0731326
Coefficient of variation (CV)0.50736153
Kurtosis0.17251079
Mean4.0861053
Median Absolute Deviation (MAD)2
Skewness0.40378385
Sum45082
Variance4.2978789
MonotonicityNot monotonic
2024-03-12T10:08:17.627671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1732
15.7%
5 1705
15.5%
6 1655
15.0%
3 1634
14.8%
4 1544
14.0%
7 1330
12.1%
1 1301
11.8%
12 77
 
0.7%
10 33
 
0.3%
9 11
 
0.1%
ValueCountFrequency (%)
1 1301
11.8%
2 1732
15.7%
3 1634
14.8%
4 1544
14.0%
5 1705
15.5%
6 1655
15.0%
7 1330
12.1%
9 11
 
0.1%
10 33
 
0.3%
11 11
 
0.1%
ValueCountFrequency (%)
12 77
 
0.7%
11 11
 
0.1%
10 33
 
0.3%
9 11
 
0.1%
7 1330
12.1%
6 1655
15.0%
5 1705
15.5%
4 1544
14.0%
3 1634
14.8%
2 1732
15.7%

starttime_day
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.013142
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:17.816094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.4981074
Coefficient of variation (CV)0.5306958
Kurtosis-1.1325926
Mean16.013142
Median Absolute Deviation (MAD)7
Skewness-0.040177852
Sum176673
Variance72.217829
MonotonicityNot monotonic
2024-03-12T10:08:18.004517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24 496
 
4.5%
9 431
 
3.9%
21 422
 
3.8%
20 421
 
3.8%
15 415
 
3.8%
18 406
 
3.7%
11 403
 
3.7%
17 389
 
3.5%
26 389
 
3.5%
14 384
 
3.5%
Other values (21) 6877
62.3%
ValueCountFrequency (%)
1 344
3.1%
2 268
2.4%
3 255
2.3%
4 321
2.9%
5 352
3.2%
6 375
3.4%
7 327
3.0%
8 336
3.0%
9 431
3.9%
10 382
3.5%
ValueCountFrequency (%)
31 194
 
1.8%
30 292
2.6%
29 279
2.5%
28 332
3.0%
27 379
3.4%
26 389
3.5%
25 364
3.3%
24 496
4.5%
23 337
3.1%
22 362
3.3%

starttime_weekday
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6651863
Minimum0
Maximum6
Zeros1605
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:18.184101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8192229
Coefficient of variation (CV)0.68258753
Kurtosis-1.0078137
Mean2.6651863
Median Absolute Deviation (MAD)1
Skewness0.17261642
Sum29405
Variance3.309572
MonotonicityNot monotonic
2024-03-12T10:08:18.340842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1939
17.6%
3 1898
17.2%
1 1807
16.4%
4 1735
15.7%
0 1605
14.5%
5 1208
10.9%
6 841
7.6%
ValueCountFrequency (%)
0 1605
14.5%
1 1807
16.4%
2 1939
17.6%
3 1898
17.2%
4 1735
15.7%
5 1208
10.9%
6 841
7.6%
ValueCountFrequency (%)
6 841
7.6%
5 1208
10.9%
4 1735
15.7%
3 1898
17.2%
2 1939
17.6%
1 1807
16.4%
0 1605
14.5%

starttime_hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.170035
Minimum0
Maximum23
Zeros116
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:18.529265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median12
Q316
95-th percentile20
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.0499052
Coefficient of variation (CV)0.41494581
Kurtosis-0.69125276
Mean12.170035
Median Absolute Deviation (MAD)4
Skewness-0.046103717
Sum134272
Variance25.501542
MonotonicityNot monotonic
2024-03-12T10:08:18.710577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
16 834
 
7.6%
7 817
 
7.4%
6 817
 
7.4%
17 810
 
7.3%
15 783
 
7.1%
8 761
 
6.9%
13 678
 
6.1%
11 637
 
5.8%
12 634
 
5.7%
9 614
 
5.6%
Other values (14) 3648
33.1%
ValueCountFrequency (%)
0 116
 
1.1%
1 88
 
0.8%
2 67
 
0.6%
3 112
 
1.0%
4 165
 
1.5%
5 213
 
1.9%
6 817
7.4%
7 817
7.4%
8 761
6.9%
9 614
5.6%
ValueCountFrequency (%)
23 144
 
1.3%
22 183
 
1.7%
21 177
 
1.6%
20 245
 
2.2%
19 331
 
3.0%
18 589
5.3%
17 810
7.3%
16 834
7.6%
15 783
7.1%
14 610
5.5%

starttime_minute
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.355479
Minimum0
Maximum59
Zeros221
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size43.2 KiB
2024-03-12T10:08:18.930245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median31
Q346
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.510227
Coefficient of variation (CV)0.57683908
Kurtosis-1.2141831
Mean30.355479
Median Absolute Deviation (MAD)15
Skewness-0.055190664
Sum334912
Variance306.60803
MonotonicityNot monotonic
2024-03-12T10:08:19.160444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 269
 
2.4%
59 227
 
2.1%
0 221
 
2.0%
41 217
 
2.0%
37 216
 
2.0%
19 213
 
1.9%
15 213
 
1.9%
52 209
 
1.9%
7 209
 
1.9%
27 206
 
1.9%
Other values (50) 8833
80.1%
ValueCountFrequency (%)
0 221
2.0%
1 175
1.6%
2 153
1.4%
3 158
1.4%
4 147
1.3%
5 199
1.8%
6 174
1.6%
7 209
1.9%
8 156
1.4%
9 138
1.3%
ValueCountFrequency (%)
59 227
2.1%
58 198
1.8%
57 202
1.8%
56 269
2.4%
55 171
1.5%
54 202
1.8%
53 183
1.7%
52 209
1.9%
51 185
1.7%
50 201
1.8%

Interactions

2024-03-12T10:07:53.904545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:32.717997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:36.766592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:40.543349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:45.536409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:49.711645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:53.787686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:57.598132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:01.722328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:05.761736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:09.824180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:13.849282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:17.883327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:21.856086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:26.072598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:30.011452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:33.920418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:37.908910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:41.789662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:45.718852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:50.156723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:54.101064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:32.916127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:36.951496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:40.736303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:45.744327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:49.899649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:53.988759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:57.804761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:01.920501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:05.951309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:10.025535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:14.052548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:18.082363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:22.047833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:26.267121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:30.206775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:34.113871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:38.113326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:41.986075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:46.409064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:50.346408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:54.286369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:33.097366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:37.114874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:40.907725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:45.932132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:50.069207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:54.159331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:57.989423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:02.098069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:06.118384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:10.206115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:14.231692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:18.259310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:22.215454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:26.439394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:30.383013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:34.286584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:38.283457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:42.163168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:46.582957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:50.516513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:54.470255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:33.283534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:37.286349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:41.080033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:46.123688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:50.240511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:54.330831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:58.179348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:02.278986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:06.289872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:10.391144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:14.416163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:18.442723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:22.388827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:26.615133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:30.555771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:34.459374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:38.462755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:42.344862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:46.760457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:50.686207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:54.688289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:33.498789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:37.487680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:42.477081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:46.341212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:50.441717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:54.530461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:58.397685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:02.494541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:06.495813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:10.607803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:14.626581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:18.655013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:22.592484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:26.823172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:30.759760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:34.678446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:38.664650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:42.554326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:46.969173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:50.887918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:54.869424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:33.684797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:37.660046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:42.650416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:46.534339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:50.611995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:54.704244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:58.590558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:02.679022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:06.667163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:10.793230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:14.810877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:18.837831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:22.769045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:27.002189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:30.948194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:34.851284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:38.849594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:42.736654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:47.146666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:51.057510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:55.046233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:33.868240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:37.827318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:42.821842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:46.725109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:50.780093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:54.867463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:58.777326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:02.857564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:06.835345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:10.971797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:14.987626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:19.014446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:22.938654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:27.176206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-03-12T10:07:35.039683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:39.025056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:42.911712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:47.323123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:51.222681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:55.249394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:34.074719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:38.019206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:43.015630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:46.941216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:50.975214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:55.057802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:58.986836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:03.064131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:07.024460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:11.180328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:15.192563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:19.219445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:23.134569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:27.375429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:31.324420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:35.252254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:39.222813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:43.113865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:47.526117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:51.415252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-03-12T10:07:21.119113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:25.357289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:29.247154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:33.203586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:37.154824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:41.058218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:44.990452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:49.413401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:53.197197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:57.348228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:36.203806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:40.005716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:44.995496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:49.129143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:52.995084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:57.030919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:01.153527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:05.188779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:09.305186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:13.288219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:17.314931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:21.307983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:25.541475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:29.442798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:33.376340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:37.348722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:41.241629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:45.176003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:49.601614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:53.374391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:57.538738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:36.395308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:40.188236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:45.179919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:49.327297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:53.181067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:57.214249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:01.347381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:05.385682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:09.484756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:13.479290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:17.509083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:21.495272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:25.722395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:29.641294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:33.564719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:37.538065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:41.427025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:45.361867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:49.790936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:53.555373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:57.717302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:36.572256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:40.356761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:45.350942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:49.511345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:53.352284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:06:57.382412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:01.526618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:05.567535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:09.646427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:13.656596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:17.689948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:21.669717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:25.889982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:29.820528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:33.737539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:37.721128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:41.599869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:45.534088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:49.966623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-12T10:07:53.720612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-12T10:08:19.470223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed 0idlongitudelatitudedurationsubCategoryAdisplaynameadviceAadviceBotherAdvicedirectionmainstreetlanessuburbcount_attendingdistance_to_CBDstarttime_monthstarttime_daystarttime_weekdaystarttime_hourstarttime_minutemaincategoryisMajorclosuretypelanesaffectedendedmulti_vehicleCrime SceneTow TruckTransport for NSWBus companyTyre fitterMechanicSydney PortsLocal CouncilNRMAHeavy Vehicle InspectorsSweeperHeavy vehicle tow truckTyre FitterUtility CompanyMotorway CrewEmergency servicesRoadside AssistanceOtherHelicopterCrash Investigation Unitstarttime_year
Unnamed 01.0000.925-0.0010.019-0.006-0.0240.042-0.033-0.0370.064-0.033-0.042-0.038-0.0120.0590.0020.968-0.012-0.001-0.0210.0220.1120.1330.0710.0610.2960.0690.0860.0580.0800.0380.0430.0590.0000.0250.0580.0510.0590.0480.0000.0420.0300.0650.0000.0650.0260.1200.111
id0.9251.000-0.0080.006-0.028-0.0680.004-0.005-0.0530.046-0.009-0.046-0.026-0.0080.0450.0000.9110.129-0.0120.0210.0190.2610.0460.0410.2680.0670.0640.0320.0550.0730.0490.0210.0170.0000.0000.0000.0000.0060.0450.0000.0380.0190.0630.0000.0370.0000.0310.865
longitude-0.001-0.0081.0000.030-0.050-0.005-0.186-0.0600.019-0.034-0.0170.083-0.0290.061-0.136-0.8660.012-0.0280.0310.0130.0220.3140.0880.0840.0820.1040.1540.0720.0610.0940.0000.0290.0550.0530.0000.0040.0300.0800.0720.0230.0940.1220.1720.0000.1080.0240.1740.259
latitude0.0190.0060.0301.0000.026-0.003-0.0670.0360.0260.0070.0020.0920.1110.135-0.0170.0800.020-0.011-0.0240.0070.0030.1290.1360.1130.0830.0740.1210.1540.0620.0990.0000.0590.0350.0900.0000.0330.0290.0250.0750.0000.0800.2120.1950.0000.1260.0580.1510.152
duration-0.006-0.028-0.0500.0261.0000.1280.091-0.038-0.0040.058-0.021-0.008-0.091-0.0340.0720.0830.011-0.0030.017-0.0480.0440.1770.1520.0900.0350.0870.0510.1580.0230.0610.0000.0000.0770.0000.0000.0000.0000.0220.1350.0000.2820.0540.0510.0000.0360.0000.2090.143
subCategoryA-0.024-0.068-0.005-0.0030.1281.0000.0410.0780.0640.0170.040-0.0130.062-0.013-0.020-0.010-0.0220.011-0.025-0.0690.0320.4880.2830.1480.1370.1930.8480.3230.1920.1000.0780.0370.4490.0120.0210.0200.0630.1260.3260.0120.0830.1180.2930.0000.1080.0610.1460.148
displayname0.0420.004-0.186-0.0670.0910.0411.000-0.0610.0330.182-0.116-0.026-0.211-0.0320.3410.2090.0200.0280.018-0.070-0.0080.7380.2500.2500.2150.2690.8770.1280.1960.1100.0000.0990.1670.0150.0150.0190.1350.1120.3270.0150.1510.1450.5250.0000.1270.0720.1970.105
adviceA-0.033-0.005-0.0600.036-0.0380.078-0.0611.0000.186-0.0110.029-0.0420.145-0.0280.0600.064-0.032-0.0110.0980.0320.0090.2390.3900.5450.2120.2390.1690.3760.0940.2180.0000.0000.0490.0780.0150.0000.0240.0000.0520.0000.1580.1300.2360.0000.5280.0330.1960.474
adviceB-0.037-0.0530.0190.026-0.0040.0640.0330.1861.0000.083-0.035-0.0090.110-0.0290.149-0.008-0.036-0.0190.035-0.0190.0140.1680.3960.4220.1700.1870.1820.2820.0690.1620.0310.0000.0340.0000.0000.0000.0230.0000.0610.0000.0540.1200.2000.0000.2930.0720.2700.159
otherAdvice0.0640.046-0.0340.0070.0580.0170.182-0.0110.0831.000-0.077-0.028-0.1170.0210.1740.0570.048-0.000-0.025-0.050-0.0150.1180.2120.1780.1150.1480.1000.0740.0940.0800.0690.0000.0280.0000.0000.0000.0260.2200.0660.0000.1400.0420.1510.0000.0540.0740.1380.000
direction-0.033-0.009-0.0170.002-0.0210.040-0.1160.029-0.035-0.0771.0000.0370.127-0.013-0.073-0.001-0.0450.001-0.0350.099-0.0030.2000.1150.2840.1920.0950.1830.0460.0390.1110.0000.0160.0820.0000.0000.0000.0270.0220.0440.0000.1510.2710.2070.0000.0460.0000.0610.127
mainstreet-0.042-0.0460.0830.092-0.008-0.013-0.026-0.042-0.009-0.0280.0371.0000.0470.020-0.005-0.028-0.046-0.0180.024-0.026-0.0040.1720.0620.0820.0810.0820.0690.0870.0760.2530.0150.0630.0690.0470.0230.0160.0610.0590.1020.0330.0570.5120.1850.0000.1190.0270.0730.190
lanes-0.038-0.026-0.0290.111-0.0910.062-0.2110.1450.110-0.1170.1270.0471.0000.0380.0740.012-0.032-0.0230.0010.008-0.0280.2010.1370.6460.4910.1080.2090.0300.1200.2560.0530.0000.1090.0000.0110.0200.0070.0330.1010.0000.0650.1740.0920.0000.4520.0000.0820.089
suburb-0.012-0.0080.0610.135-0.034-0.013-0.032-0.028-0.0290.021-0.0130.0200.0381.000-0.039-0.058-0.0150.033-0.0260.024-0.0030.1440.0470.0640.0830.0470.0570.0980.0760.1080.0060.0430.0570.0260.0170.0170.0870.0570.0660.0220.0500.1970.0970.0280.0990.0350.0960.132
count_attending0.0590.045-0.136-0.0170.072-0.0200.3410.0600.1490.174-0.073-0.0050.074-0.0391.0000.1590.0330.021-0.008-0.0600.0020.3240.2820.1990.1220.2260.2390.2980.5530.4820.0000.0310.1600.0000.0390.0320.1960.1750.2800.0000.2660.1180.6120.0000.3600.0880.3200.707
distance_to_CBD0.0020.000-0.8660.0800.083-0.0100.2090.064-0.0080.057-0.001-0.0280.012-0.0580.1591.000-0.0100.023-0.043-0.036-0.0100.1710.1000.0720.0760.0880.1620.0670.0620.0930.0100.0370.0510.0210.0210.0000.0730.0410.0850.0150.0960.1370.1960.0000.0950.0230.0900.129
starttime_month0.9680.9110.0120.0200.011-0.0220.020-0.032-0.0360.048-0.045-0.046-0.032-0.0150.033-0.0101.000-0.0190.003-0.0110.0240.5570.0700.1060.1210.1140.0790.0540.0640.1290.0000.0350.0540.0170.0170.0450.0440.0440.0310.0000.0470.0910.0960.0000.1580.0180.0890.672
starttime_day-0.0120.129-0.028-0.011-0.0030.0110.028-0.011-0.019-0.0000.001-0.018-0.0230.0330.0210.023-0.0191.000-0.039-0.0020.0230.1430.0450.0610.0630.0300.0170.0760.0520.0120.0220.0450.0350.0260.0280.0190.0590.0500.0530.0260.0610.0170.0510.0000.0540.0350.0780.150
starttime_weekday-0.001-0.0120.031-0.0240.017-0.0250.0180.0980.035-0.025-0.0350.0240.001-0.026-0.008-0.0430.003-0.0391.0000.025-0.0290.1060.0600.0500.0540.0540.0200.1070.0440.0770.0080.0310.0660.0230.0410.0270.0460.0360.0870.0230.0450.0230.1440.0140.0810.0300.0580.107
starttime_hour-0.0210.0210.0130.007-0.048-0.069-0.0700.032-0.019-0.0500.099-0.0260.0080.024-0.060-0.036-0.011-0.0020.0251.0000.0010.1710.0890.1300.0920.1210.1190.0810.0650.0260.0000.0280.0390.0500.0310.0270.0530.0250.0870.0550.0570.0240.1960.0000.1330.0290.1190.158
starttime_minute0.0220.0190.0220.0030.0440.032-0.0080.0090.014-0.015-0.003-0.004-0.028-0.0030.002-0.0100.0240.023-0.0290.0011.0000.1370.0720.0650.0720.0390.0220.1100.0310.0470.0190.0440.0380.0240.0310.0250.0580.0490.0300.0300.0980.0350.0370.0100.0500.0210.0970.129
maincategory0.1120.2610.3140.1290.1770.4880.7380.2390.1680.1180.2000.1720.2010.1440.3240.1710.5570.1430.1060.1710.1371.0000.1890.2280.2820.2010.7440.0490.1600.0630.0000.0040.1590.0030.0030.0290.0270.0140.1600.0030.1380.1240.5220.0000.1360.0280.1000.434
isMajor0.1330.0460.0880.1360.1520.2830.2500.3900.3960.2120.1150.0620.1370.0470.2820.1000.0700.0450.0600.0890.0720.1891.0000.3040.1620.1850.1820.1390.0810.0850.0000.0000.0320.0000.0000.0000.0830.0000.0180.0000.0000.0260.1290.0000.0220.0380.2330.023
closuretype0.0710.0410.0840.1130.0900.1480.2500.5450.4220.1780.2840.0820.6460.0640.1990.0720.1060.0610.0500.1300.0650.2280.3041.0000.6610.2070.2190.0690.1080.3170.0000.0050.0890.0150.0000.0000.0200.0060.1140.0000.1100.1030.2440.0040.5290.0260.2360.062
lanesaffected0.0610.2680.0820.0830.0350.1370.2150.2120.1700.1150.1920.0810.4910.0830.1220.0760.1210.0630.0540.0920.0720.2820.1620.6611.0000.1900.2950.0480.1720.2440.0000.0000.0960.0000.0000.0000.0160.0000.1360.0000.0760.1450.2200.0000.4910.0280.0930.260
ended0.2960.0670.1040.0740.0870.1930.2690.2390.1870.1480.0950.0820.1080.0470.2260.0880.1140.0300.0540.1210.0390.2010.1850.2070.1901.0000.1280.0390.0720.0390.0000.0050.0220.0000.0000.0130.0200.0000.0830.0000.0620.0020.1150.0000.0490.0220.0840.105
multi_vehicle0.0690.0640.1540.1210.0510.8480.8770.1690.1820.1000.1830.0690.2090.0570.2390.1620.0790.0170.0200.1190.0220.7440.1820.2190.2950.1281.0000.0450.1210.0390.0000.0180.0950.0000.0000.0230.0300.0140.0870.0000.0480.0880.3580.0000.0740.0260.0730.058
Crime Scene0.0860.0320.0720.1540.1580.3230.1280.3760.2820.0740.0460.0870.0300.0980.2980.0670.0540.0760.1070.0810.1100.0490.1390.0690.0480.0390.0451.0000.0750.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0520.0000.0160.0000.0000.000
Tow Truck0.0580.0550.0610.0620.0230.1920.1960.0940.0690.0940.0390.0760.1200.0760.5530.0620.0640.0520.0440.0650.0310.1600.0810.1080.1720.0720.1210.0751.0000.0510.0000.0000.0330.0000.0000.0000.0000.0000.0720.0000.0350.0400.1800.0000.1530.0000.0360.009
Transport for NSW0.0800.0730.0940.0990.0610.1000.1100.2180.1620.0800.1110.2530.2560.1080.4820.0930.1290.0120.0770.0260.0470.0630.0850.3170.2440.0390.0390.0380.0511.0000.0000.0260.0150.0000.0000.0000.0000.0070.0370.0000.0560.3430.1430.0000.4750.0160.0770.055
Bus company0.0380.0490.0000.0000.0000.0780.0000.0000.0310.0690.0000.0150.0530.0060.0000.0100.0000.0220.0080.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.000
Tyre fitter0.0430.0210.0290.0590.0000.0370.0990.0000.0000.0000.0160.0630.0000.0430.0310.0370.0350.0450.0310.0280.0440.0040.0000.0050.0000.0050.0180.0000.0000.0260.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0200.0000.0000.0000.0000.000
Mechanic0.0590.0170.0550.0350.0770.4490.1670.0490.0340.0280.0820.0690.1090.0570.1600.0510.0540.0350.0660.0390.0380.1590.0320.0890.0960.0220.0950.0000.0330.0150.0000.0001.0000.0000.0570.0000.0380.0000.0860.0000.0170.0270.1040.0000.0700.0000.0120.016
Sydney Ports0.0000.0000.0530.0900.0000.0120.0150.0780.0000.0000.0000.0470.0000.0260.0000.0210.0170.0260.0230.0500.0240.0030.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Local Council0.0250.0000.0000.0000.0000.0210.0150.0150.0000.0000.0000.0230.0110.0170.0390.0210.0170.0280.0410.0310.0310.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
NRMA0.0580.0000.0040.0330.0000.0200.0190.0000.0000.0000.0000.0160.0200.0170.0320.0000.0450.0190.0270.0270.0250.0290.0000.0000.0000.0130.0230.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.000
Heavy Vehicle Inspectors0.0510.0000.0300.0290.0000.0630.1350.0240.0230.0260.0270.0610.0070.0870.1960.0730.0440.0590.0460.0530.0580.0270.0830.0200.0160.0200.0300.0000.0000.0000.0000.0000.0380.0000.0000.0001.0000.0000.0820.0000.0000.0000.0280.0000.0000.0000.0000.000
Sweeper0.0590.0060.0800.0250.0220.1260.1120.0000.0000.2200.0220.0590.0330.0570.1750.0410.0440.0500.0360.0250.0490.0140.0000.0060.0000.0000.0140.0000.0000.0070.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0410.0120.0000.0000.0000.0000.000
Heavy vehicle tow truck0.0480.0450.0720.0750.1350.3260.3270.0520.0610.0660.0440.1020.1010.0660.2800.0850.0310.0530.0870.0870.0300.1600.0180.1140.1360.0830.0870.0000.0720.0370.0000.0000.0860.0000.0000.0000.0820.0001.0000.0000.0150.1090.0910.0000.0940.0000.0000.024
Tyre Fitter0.0000.0000.0230.0000.0000.0120.0150.0000.0000.0000.0000.0330.0000.0220.0000.0150.0000.0260.0230.0550.0300.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0230.0000.0000.0000.0000.0000.000
Utility Company0.0420.0380.0940.0800.2820.0830.1510.1580.0540.1400.1510.0570.0650.0500.2660.0960.0470.0610.0450.0570.0980.1380.0000.1100.0760.0620.0480.0000.0350.0560.0000.0000.0170.0000.0000.0000.0000.0000.0150.0001.0000.0310.0460.0000.0330.0000.0000.000
Motorway Crew0.0300.0190.1220.2120.0540.1180.1450.1300.1200.0420.2710.5120.1740.1970.1180.1370.0910.0170.0230.0240.0350.1240.0260.1030.1450.0020.0880.0150.0400.3430.0000.0630.0270.0000.0000.0000.0000.0410.1090.0230.0311.0000.2030.0000.1600.0000.0370.018
Emergency services0.0650.0630.1720.1950.0510.2930.5250.2360.2000.1510.2070.1850.0920.0970.6120.1960.0960.0510.1440.1960.0370.5220.1290.2440.2200.1150.3580.0520.1800.1430.0030.0200.1040.0000.0000.0100.0280.0120.0910.0000.0460.2031.0000.0000.3830.0300.1080.026
Roadside Assistance0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0140.0000.0100.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
Other0.0650.0370.1080.1260.0360.1080.1270.5280.2930.0540.0460.1190.4520.0990.3600.0950.1580.0540.0810.1330.0500.1360.0220.5290.4910.0490.0740.0160.1530.4750.0000.0000.0700.0000.0000.0000.0000.0000.0940.0000.0330.1600.3830.0001.0000.0000.0390.103
Helicopter0.0260.0000.0240.0580.0000.0610.0720.0330.0720.0740.0000.0270.0000.0350.0880.0230.0180.0350.0300.0290.0210.0280.0380.0260.0280.0220.0260.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0001.0000.0000.000
Crash Investigation Unit0.1200.0310.1740.1510.2090.1460.1970.1960.2700.1380.0610.0730.0820.0960.3200.0900.0890.0780.0580.1190.0970.1000.2330.2360.0930.0840.0730.0000.0360.0770.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0370.1080.0000.0390.0001.0000.000
starttime_year0.1110.8650.2590.1520.1430.1480.1050.4740.1590.0000.1270.1900.0890.1320.7070.1290.6720.1500.1070.1580.1290.4340.0230.0620.2600.1050.0580.0000.0090.0550.0000.0000.0160.0000.0000.0000.0000.0000.0240.0000.0000.0180.0260.0000.1030.0000.0001.000

Missing values

2024-03-12T10:07:58.154179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-12T10:07:59.325791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed 0idmaincategorylongitudelatitudedurationsubCategoryAsubCategoryBdisplaynameisMajoradviceAadviceBotherAdviceclosuretypedirectionmainstreetlanesaffectedlanessuburbendedisnewincidentcount_attendingdistance_to_CBDmulti_vehicleCrime SceneTow TruckTransport for NSWBus companyTyre fitterMechanicSydney PortsLocal CouncilNRMAHeavy Vehicle InspectorsSweeperHeavy vehicle tow truckTyre FitterUtility CompanyMotorway CrewEmergency servicesRoadside AssistanceOtherHelicopterCrash Investigation Unitstarttime_yearstarttime_monthstarttime_daystarttime_weekdaystarttime_hourstarttime_minute
011096571151.112224-33.86967126.506333105106290234271.03.0481028.74576010100000000000001000020221301538
121097400151.138848-33.88608192.07150014050600234271.04.02841016.43234200010000000000000000020221411952
231097751150.917528-33.93056426.93661714076013120252202.03.017110227.48667200100000000000001000020221521118
341097970150.980470-33.92913967.8980503303301120272071.05.019110221.81926300010000000000001000020221521535
451097990151.098833-33.867937145.92938333033010003427-1.0-1.0731019.99322900000000000000000010020221521541
561100061151.059994-34.08142744.48971714076060001474-1.0-1.026110126.77514800000000000000001000020221741336
671102421150.958972-33.96017113.00405036013001312007204-1.0-1.013710224.85085810010000000000001000020221111735
781104141151.298005-33.744830182.68383310510629007438-1.0-1.07210116.565837100000000000000010000202211331011
891105640151.131191-33.87668915.97605014050000285501.02.01301016.99199700000000000000000010020221144164
9101106970151.213335-33.84923016.893567140506290275051.02.02161012.73456200010000000000000000020221166141
Unnamed 0idmaincategorylongitudelatitudedurationsubCategoryAsubCategoryBdisplaynameisMajoradviceAadviceBotherAdviceclosuretypedirectionmainstreetlanesaffectedlanessuburbendedisnewincidentcount_attendingdistance_to_CBDmulti_vehicleCrime SceneTow TruckTransport for NSWBus companyTyre fitterMechanicSydney PortsLocal CouncilNRMAHeavy Vehicle InspectorsSweeperHeavy vehicle tow truckTyre FitterUtility CompanyMotorway CrewEmergency servicesRoadside AssistanceOtherHelicopterCrash Investigation Unitstarttime_yearstarttime_monthstarttime_daystarttime_weekdaystarttime_hourstarttime_minute
1102317086431552151.034000-33.72150033.93050000460436250137-1.0-1.023400023.2145131000000000000000000002019111431139
1102417089956591151.073818-33.85546712.94150036013006200272134.05.013810312.448438101100000000000010000202162431318
11025170911095471151.074050-33.8063237.544550410480610285272.03.031300114.343471000100000000000000000202211518
11026170951019761151.021004-33.831351109.57311741076013120232932.04.07100217.79789900010000000000001000020211010633
11027170961019761151.021004-33.831351113.10583341076013120232932.04.07100217.79789900010000000000001000020211010633
11028170981082522151.266388-33.87148331.254567410470112003390-1.0-1.02550015.5304880000000000000000001002021121521156
11029170991082522151.266388-33.87148366.139667410470112003390-1.0-1.02550015.5304880000000000000000001002021121521156
11030171001082522151.266388-33.871483126.496500410470112003390-1.0-1.02550015.5304880000000000000000001002021121521156
11031171011082522151.266388-33.871483220.781667410470112003390-1.0-1.02550015.5304880000000000000000001002021121521156
11032171021082522151.266388-33.871483267.197567410470112003390-1.0-1.02550015.5304880000000000000000001002021121521156